Language Data Scientist

Language Data Scientist at Innodata Inc.

Innodata (NASDAQ: INOD) is a leading data engineering company renowned for its AI technology solutions. Serving over 2,000 customers globally, including 4 out of 5 of the world’s biggest technology companies, Innodata combines advanced machine learning and artificial intelligence (ML/AI) with a global workforce of subject matter experts and a high-security infrastructure. With over 7,000 employees across 13 cities worldwide, Innodata is poised for explosive growth, helping usher in the promise of AI through digital data solutions and high-quality platforms.

About the Role

Innodata is seeking a Language Data Scientist to join a growing team of Gen AI experts. This role involves hands-on work with multi-modal and multi-lingual datasets, collaborating with cross-functional partners, and leveraging expertise in human and synthetic data workflows to drive innovation. The ideal candidate will possess a strong blend of skills in computational linguistics, human evaluation tasks, data science, and data engineering, contributing to the advancement of GenAI applications for Innodata's customers.

Key Responsibilities

• Design and improve workflows for AI/ML training and evaluation data, including human annotation and data collection, as well as synthetic data generation.
• Deeply analyze existing workflows and processes to gather insights, propose recommendations, and implement improvements through innovation and cross-functional collaboration with customers.
• Critically assess annotation tooling and workflows to optimize efficiency and quality.
• Perform quantitative analysis on large datasets, applying statistical methods, calculating metrics, and making data-driven recommendations for accuracy and performance enhancements.
• Engage closely with client stakeholders to understand objectives, gather requirements, propose effective solutions, and ensure successful execution.

Qualifications

• MA in computational linguistics, data science, computer science (AI/ML/NLU), quantitative social sciences, or a related scientific/quantitative field (PhD strongly preferred).
• Extensive experience with human language data, designing complex multi-phase human evaluation tasks, and a deep understanding of language-culture relationships.
• Ability to identify and resolve ambiguity and subjectivity in language across multi-lingual and multi-modal projects.
• Advanced knowledge of statistics, metrics (e.g., f1 score, inter-rater reliability), and data analysis methods like sampling.
• Proficiency in Python for handling and transforming large datasets (preprocessing, postprocessing, pandas), performing quantitative analyses, and data visualization (matplotlib, seaborn).
• Deep understanding of data pipelines for ML and NLP workflows, including efficient data collection, transformation, storage, data structures, algorithms, and data engineering principles.
• Excellent interpersonal skills for effective cross-functional stakeholder engagement and problem-solving.
• Ability to work independently, collaborate effectively in a team, and adapt to evolving technologies and methodologies.
• Capacity to translate research and development insights to understand client products and services.

Preferred Qualifications

• Stay up-to-date with the latest advancements in generative AI, machine learning, and deep learning techniques through continuous research.
• Knowledge of optimizing existing generative AI models for improved performance, scalability, and efficiency.
• Experience in developing and maintaining ML/AI pipelines, encompassing data preprocessing, feature extraction, model training, and evaluation.
• Familiarity with fine-tuning pre-trained models for specific tasks and datasets.
• Ability to create clear documentation for technical specifications, user guides, and presentations, effectively communicating complex AI concepts.
• Contribute to establishing best practices for generative AI development both internally and with customers.
• Provide technical mentorship and guidance to junior team members.
• Understanding of techniques such as GPT, VAE, and GANs.

Key skills/competency

• Computational Linguistics
• Data Science
• Natural Language Processing (NLP)
• Generative AI (GenAI)
• Python Programming
• Statistical Analysis
• Data Engineering
• Machine Learning (ML)
• Human Annotation
• Multi-lingual Data